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  Semi-supervised Analysis of Human fMRI Data

Shelton, J., Blaschko, M., Lampert, C., & Bartels, A. (2009). Semi-supervised Analysis of Human fMRI Data. Poster presented at Berlin BCI Workshop 2009: Advances in Nanotechnology, Berlin, Germany.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C41F-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-13CE-0
Genre: Poster

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 Creators:
Shelton, JA1, 2, Author              
Blaschko, MB, Author              
Lampert, CH1, 2, Author              
Bartels, A2, 3, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              

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 Abstract: Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, CCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, CCA may suffer from small sample effects. We propose to use semisupervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of CCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing.

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 Dates: 2009-07
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 5935
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Title: Berlin BCI Workshop 2009: Advances in Nanotechnology
Place of Event: Berlin, Germany
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